Enhancing interpretability in power management: A time-encoded household energy forecasting using hybrid deep learning model-Reference-Cited by-同舟云学术

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Enhancing interpretability in power management: A time-encoded household energy forecasting using hybrid deep learning model

Published:2024-09 Issue: Volume:315 Page:118795
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Mubarak Hamza^ORCID,Stegen Sascha,Bai Feifei,Abdellatif Abdallah,Sanjari Mohammad J.

Publisher

Elsevier BV

Reference54 articles.

1. Data-driven load profiles and the dynamics of residential electricity consumption;Anvari;Nat Commun,2022

2. Iot task management mechanism based on predictive optimization for efficient energy consumption in smart residential buildings;Iqbal;Energ Buildings,2022

3. Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study;Mubarak;Energies,2023

4. A segmented evaluation model for building energy performance considering seasonal dynamic fluctuations;Zhang;Energ Conver Manage,2023

5. A review of the-state-of-the-art in data-driven approaches for building energy prediction;Sun;Energ Buildings,2020

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